Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms hav...Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.展开更多
Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Ar...Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).展开更多
Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity...Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.展开更多
This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schem...This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.展开更多
Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend o...Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.展开更多
A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble...A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.展开更多
The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to a...The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.展开更多
Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell ...Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.展开更多
With increasing drilling depth and large dosage of weighting materials,drilling fluids with high solid content are characterized by poor stability,high viscosity,large water loss,and thick mud cake,easier leading to r...With increasing drilling depth and large dosage of weighting materials,drilling fluids with high solid content are characterized by poor stability,high viscosity,large water loss,and thick mud cake,easier leading to reservoir damage and wellbore instability.In this paper,micronized barite(MB)was modified(mMB)by grafting with hydrophilic polymer onto the surface through the free radical polymerization to displace conventional API barite partly.The suspension stability of water-based drilling fluids(WBDFs)weighted with API barite:mMB=2:1 in 600 g was significantly enhanced compared with that with API barite/WBDFs,exhibiting the static sag factor within 0.54 and the whole stability index of 2.The viscosity and yield point reached the minimum,with a reduction of more than 40%compared with API barite only at the same density.Through multi-stage filling and dense accumulation of weighting materials and clays,filtration loss was decreased,mud cake quality was improved,and simultaneously it had great reservoir protection performance,and the permeability recovery rate reached 87%.In addition,it also effectively improved the lubricity of WBDFs.The sticking coefficient of mud cake was reduced by 53.4%,and the friction coefficient was 0.2603.Therefore,mMB can serve as a versatile additive to control the density,rheology,filtration,and stability of WBDFs weighted with API barite,thus regulating comprehensive performance and achieving reservoir protection capacity.This work opened up a new path for the productive drilling of extremely deep and intricate wells by providing an efficient method for managing the performance of high-density WBDFs.展开更多
In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face dete...In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.展开更多
Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(...Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(RIL)population derived from a cross between W7268 and Chuanyu 12(CY12)was employed to detect quantitative trait loci(QTLs)for thousand-grain weight(TGW),grain length(GL),grain width(GW),and the ratio of grain length to width(GLW)in six environments.Seven major QTLs,QGl.cib-2D,QGw.cib-2D,QGw.cib-3B,QGw.cib-4B.1,QGlw.cib-2D.1,QTgw.cib-2D.1 and QTgw.cib-3B.1,were consistently identified in at least four environments and the best linear unbiased estimation(BLUE)datasets,and they explained 2.61 to 34.85%of the phenotypic variance.Significant interactions were detected between the two major TGW QTLs and three major GW loci.In addition,QTgw.cib-3B.1 and QGw.cib-3B were co-located,and the improved TGW at this locus was contributed by GW.Unlike other loci,QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike(GNS).They were further validated in advanced lines using Kompetitive Allele Specific PCR(KASP)markers,and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus.Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars.According to gene annotation,spatial expression patterns,ortholog analysis and sequence variation,the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted.Collectively,the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.展开更多
Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It...Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.展开更多
The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use ...The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.展开更多
Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already fe...Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.展开更多
Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is s...Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).展开更多
Objective:Postpartum weight retention(PPWR)is a common problem among women after childbirth.The main objectives of this study are to understand the changes in body weight of breastfeeding mothers during long-term foll...Objective:Postpartum weight retention(PPWR)is a common problem among women after childbirth.The main objectives of this study are to understand the changes in body weight of breastfeeding mothers during long-term follow-up and preliminarily explore the relationship between maternal body weight and human milk composition,including macronutrients,leptin,and adiponectin.Methods:The study included a longitudinal cohort(122 mothers),and a cross-sectional cohort(37 mothers).The human milk,maternal weight,and dietary surveys were collected in the longitudinal cohort at different follow-up time points(1-14 days postpartum,2-4 months postpartum,5-7 months postpartum,and 12-17 months postpartum).The maternal body weight was analyzed using the responses in the survey questionnaires.A milk analyzer based on the mid-infrared spectroscopy(MIRS)was used to determine milk composition,and nutrition analysis software evaluated dietary intakes.In the cross-sectional cohort,participating mothers were asked to provide blood and human milk samples and pertinent information related to maternal body composition.Maternal body composition was measured by bioelectrical impedance analysis(BIA),while ELISA analyzed leptin and adiponectin in milk and serum.Results:At 5-7 months postpartum,the PPWR of breastfeeding mothers was(2.46±3.59)kg.At 12-17 months postpartum,the PPWR was(0.98±4.06)kg.PPWR was found to be negatively correlated with milk fat content within 14 days postpartum and positively correlated at 2-4 months postpartum.In addition,the maternal weight and body muscle mass were positively correlated with leptin and adiponectin in milk.Plasma leptin was positively correlated with the mother’s body weight,body mass index(BMI),FAT percentage,and body fat mass,while plasma adiponectin did not correlate with any parameter.The results also indicate that the PPWR did not correlate with leptin and adiponectin in plasma or milk.Conclusions:Breastfeeding mothers may retain considerable weight gain one year after delivery.Human milk composition may be related to changes in maternal body weight.Leptin and adiponectin in breast milk and leptin in plasma are associated with the maternal body composition.This study supports the notion that maternal nutritional status may affect offspring health through lactation,and future research should focus on exploring weight management of postpartum mothers.展开更多
This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance compariso...This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.展开更多
By replacing hexyl chains in poly(3-hexylthiophene)(P3HT)with 2-propoxyethyls,four poly(3-(2-propoxyethyl)thiophene)(P3POET)homopolymers with comparable polydispersity indexes(PDI)and regioregularities were prepared h...By replacing hexyl chains in poly(3-hexylthiophene)(P3HT)with 2-propoxyethyls,four poly(3-(2-propoxyethyl)thiophene)(P3POET)homopolymers with comparable polydispersity indexes(PDI)and regioregularities were prepared herein in addition with step increment of about 7 kDa on numberaverage molecular weight(M_(n))from around 11 to 32 kDa(accordingly denoted as P11k,P18k,P25k,and P32k).When doped in film by FeCl_(3)at the optimized conditions,the maximum power factor(PF_(max))increases greatly from 4.3μW·m^(-1)·K^(-2)for P11k to 8.8μW·m^(-1)·K^(-2)for P18k,and further to 9.7μW·m^(-1)·K^(-2)for P25k,followed by a slight decrease to 9.2μW·m^(-1)·K^(-2)for P32k.The close Seebeck coefficients(S)at PF_(max)are observed in these doped polymer films due to their consistent frontier orbital energy levels and Fermi levels.The main contribution to this PF_(max)evolution thus comes from the corresponding conductivities(σ).Theσvariation of the doped films can be rationally correlated with their microstructure evolution.展开更多
Grain size is a key factor influencing grain weight in rice.In this study,a chromosome segment substitution line(CSSL9-17)was identified,that exhibits a significant reduction in both grain size and weight compared to ...Grain size is a key factor influencing grain weight in rice.In this study,a chromosome segment substitution line(CSSL9-17)was identified,that exhibits a significant reduction in both grain size and weight compared to its donor parent 93-11.Further investigation identified two quantitative trait loci(QTL)on chromosome 11,designated qGW11a and qGW11b,which contribute to 1000-grain weight with an additive effect.LOC_Os11g05690,encoding the amino acid permease OsCAT8,is the target gene of qGW11a.Overexpression of OsCAT8 resulted in decreased grain weight,while OsCAT8 knockout mutants exhibited increased grain weight.The 287-bp located within the OsCAT8 promoter region of 93-11 negatively regulates its activity,which is subsequently correlated with an increase in grain size and weight.These results suggest that OsCAT8 functions as a negative regulator of grain size and grain weight in rice.展开更多
Background: Obesity has become a serious global public health challenge, given that it leads to various adverse health outcomes that include cardiovascular illnesses, diabetes, and certain types of cancer. The World H...Background: Obesity has become a serious global public health challenge, given that it leads to various adverse health outcomes that include cardiovascular illnesses, diabetes, and certain types of cancer. The World Health Organization (WHO) has estimated that, at the end of 2022, 1 out of every 8 individuals were obese, and that the global adult obesity rates have over doubled since 1990, even as the adolescent obesity rates have quadrupled. Thus, as of 2022, nearly 2.5 billion adults, aged 18 years and above, were overweight, with 890 million being obese. Obesity and overweight incidence rate has been gradually increasing over the years, presenting significant challenges to the healthcare systems throughout the globe. In this regard, the objective of this systematic review was to evaluate the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. Methodology: To attain the above stated study objective, a systematic evaluation of previous studies was carried out, particularly studies that assessed the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. The authors have used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in the selection of eligible studies for inclusion in the study. Results: The findings indicate that lifestyle interventions resulted in 5% - 10% weight reduction and significant improvements in metabolic indicators, while pharmacotherapy (GLP-1 receptor agonists) achieved up to 15% weight reduction and considerable metabolic health benefits. Further, comparative studies show lifestyle modifications provide overall health benefits, while medication is necessary for non-responders. Conclusion: Individualized treatment strategies are crucial, and further research is needed on long-term consequences and combination therapies.展开更多
文摘Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.
基金supported by the Chinese–Norwegian Collaboration Projects within Climate Systems jointly funded by the National Key Research and Development Program of China (Grant No.2022YFE0106800)the Research Council of Norway funded project,MAPARC (Grant No.328943)+2 种基金the support from the Research Council of Norway funded project,COMBINED (Grant No.328935)the National Natural Science Foundation of China (Grant No.42075030)the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_1314)。
文摘Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).
基金This study was supported by the National Natural Science Foundation of China(61911540482 and 61702324).
文摘Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.
文摘This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.
基金funded by the Science and Technology Project of China Southern Power Grid(YNKJXM20210175)the National Natural Science Foundation of China(52177070).
文摘Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.
基金Project supported by the National Key Research and Development Program of China (Grant No.2021YFB3900701)the Science and Technology Plan Project of the State Administration for Market Regulation of China (Grant No.2023MK178)the National Natural Science Foundation of China (Grant No.42227802)。
文摘A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.
基金the National Social Science Foundation of China(Grant No.22BTJ035).
文摘The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.
基金Project supported by the National Natural Science Foundation of China(Grant No.31971183).
文摘Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.
基金This work was supported by the National Natural Science Foundation of China(Grant No.51991361)the foundation of China University of Petroleum(Beijing)(Grant No.2462021YXZZ002).
文摘With increasing drilling depth and large dosage of weighting materials,drilling fluids with high solid content are characterized by poor stability,high viscosity,large water loss,and thick mud cake,easier leading to reservoir damage and wellbore instability.In this paper,micronized barite(MB)was modified(mMB)by grafting with hydrophilic polymer onto the surface through the free radical polymerization to displace conventional API barite partly.The suspension stability of water-based drilling fluids(WBDFs)weighted with API barite:mMB=2:1 in 600 g was significantly enhanced compared with that with API barite/WBDFs,exhibiting the static sag factor within 0.54 and the whole stability index of 2.The viscosity and yield point reached the minimum,with a reduction of more than 40%compared with API barite only at the same density.Through multi-stage filling and dense accumulation of weighting materials and clays,filtration loss was decreased,mud cake quality was improved,and simultaneously it had great reservoir protection performance,and the permeability recovery rate reached 87%.In addition,it also effectively improved the lubricity of WBDFs.The sticking coefficient of mud cake was reduced by 53.4%,and the friction coefficient was 0.2603.Therefore,mMB can serve as a versatile additive to control the density,rheology,filtration,and stability of WBDFs weighted with API barite,thus regulating comprehensive performance and achieving reservoir protection capacity.This work opened up a new path for the productive drilling of extremely deep and intricate wells by providing an efficient method for managing the performance of high-density WBDFs.
文摘In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.
基金supported by the Major Program of National Agricultural Science and Technology of China(NK20220607)the West Light Foundation of the Chinese Academy of Sciences(2022XBZG_XBQNXZ_A_001)the Sichuan Science and Technology Program,China(2022ZDZX0014)。
文摘Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(RIL)population derived from a cross between W7268 and Chuanyu 12(CY12)was employed to detect quantitative trait loci(QTLs)for thousand-grain weight(TGW),grain length(GL),grain width(GW),and the ratio of grain length to width(GLW)in six environments.Seven major QTLs,QGl.cib-2D,QGw.cib-2D,QGw.cib-3B,QGw.cib-4B.1,QGlw.cib-2D.1,QTgw.cib-2D.1 and QTgw.cib-3B.1,were consistently identified in at least four environments and the best linear unbiased estimation(BLUE)datasets,and they explained 2.61 to 34.85%of the phenotypic variance.Significant interactions were detected between the two major TGW QTLs and three major GW loci.In addition,QTgw.cib-3B.1 and QGw.cib-3B were co-located,and the improved TGW at this locus was contributed by GW.Unlike other loci,QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike(GNS).They were further validated in advanced lines using Kompetitive Allele Specific PCR(KASP)markers,and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus.Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars.According to gene annotation,spatial expression patterns,ortholog analysis and sequence variation,the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted.Collectively,the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.
文摘Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.
基金This work was partly supported by the Basic Ability Improvement Project for Young andMiddle-aged Teachers in Guangxi Colleges andUniversities(2021KY1800,2021KY1804).
文摘The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.
基金This work was supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Project no.GRANT 324).
文摘Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.
基金supported by the Notional Natural Science Foundation of China,No.81960417 (to JX)Guangxi Key Research and Development Program,No.GuiKeA B20159027 (to JX)the Natural Science Foundation of Guangxi Zhuang Autonomous Region,No.2022GXNSFBA035545 (to YG)。
文摘Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).
基金supported by grants from the Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition(17dz2272000)Foundation of Shanghai Municipal Health Commission(Key weak discipline construction project 2019ZB0101)the Scientific research fund of China Nutrition Society(CNSHPNK2021-16)。
文摘Objective:Postpartum weight retention(PPWR)is a common problem among women after childbirth.The main objectives of this study are to understand the changes in body weight of breastfeeding mothers during long-term follow-up and preliminarily explore the relationship between maternal body weight and human milk composition,including macronutrients,leptin,and adiponectin.Methods:The study included a longitudinal cohort(122 mothers),and a cross-sectional cohort(37 mothers).The human milk,maternal weight,and dietary surveys were collected in the longitudinal cohort at different follow-up time points(1-14 days postpartum,2-4 months postpartum,5-7 months postpartum,and 12-17 months postpartum).The maternal body weight was analyzed using the responses in the survey questionnaires.A milk analyzer based on the mid-infrared spectroscopy(MIRS)was used to determine milk composition,and nutrition analysis software evaluated dietary intakes.In the cross-sectional cohort,participating mothers were asked to provide blood and human milk samples and pertinent information related to maternal body composition.Maternal body composition was measured by bioelectrical impedance analysis(BIA),while ELISA analyzed leptin and adiponectin in milk and serum.Results:At 5-7 months postpartum,the PPWR of breastfeeding mothers was(2.46±3.59)kg.At 12-17 months postpartum,the PPWR was(0.98±4.06)kg.PPWR was found to be negatively correlated with milk fat content within 14 days postpartum and positively correlated at 2-4 months postpartum.In addition,the maternal weight and body muscle mass were positively correlated with leptin and adiponectin in milk.Plasma leptin was positively correlated with the mother’s body weight,body mass index(BMI),FAT percentage,and body fat mass,while plasma adiponectin did not correlate with any parameter.The results also indicate that the PPWR did not correlate with leptin and adiponectin in plasma or milk.Conclusions:Breastfeeding mothers may retain considerable weight gain one year after delivery.Human milk composition may be related to changes in maternal body weight.Leptin and adiponectin in breast milk and leptin in plasma are associated with the maternal body composition.This study supports the notion that maternal nutritional status may affect offspring health through lactation,and future research should focus on exploring weight management of postpartum mothers.
基金Supported by the National Natural Science Foundation of China(12071335)the Humanities and Social Science Research Projects in Ministry of Education(20YJAZH025).
文摘This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.
基金Funded by the State Key Laboratory of Advanced Technology for Materials Synthesis and Processing,Wuhan Univesity of Technology。
文摘By replacing hexyl chains in poly(3-hexylthiophene)(P3HT)with 2-propoxyethyls,four poly(3-(2-propoxyethyl)thiophene)(P3POET)homopolymers with comparable polydispersity indexes(PDI)and regioregularities were prepared herein in addition with step increment of about 7 kDa on numberaverage molecular weight(M_(n))from around 11 to 32 kDa(accordingly denoted as P11k,P18k,P25k,and P32k).When doped in film by FeCl_(3)at the optimized conditions,the maximum power factor(PF_(max))increases greatly from 4.3μW·m^(-1)·K^(-2)for P11k to 8.8μW·m^(-1)·K^(-2)for P18k,and further to 9.7μW·m^(-1)·K^(-2)for P25k,followed by a slight decrease to 9.2μW·m^(-1)·K^(-2)for P32k.The close Seebeck coefficients(S)at PF_(max)are observed in these doped polymer films due to their consistent frontier orbital energy levels and Fermi levels.The main contribution to this PF_(max)evolution thus comes from the corresponding conductivities(σ).Theσvariation of the doped films can be rationally correlated with their microstructure evolution.
基金supported by grants from the National Natural Science Foundation of China(32325038)the Postdoctoral Fellowship Program of CPSF(GZB20230499)+1 种基金the Sichuan Science and Technology Program(24NSFSC4494)the Open Project Program(SKL-ZY202212)of State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China.We thank the High-Performance Computing Platform of Sichuan Agricultural University for its support for the analysis of substitution segments in CSSL9-17.
文摘Grain size is a key factor influencing grain weight in rice.In this study,a chromosome segment substitution line(CSSL9-17)was identified,that exhibits a significant reduction in both grain size and weight compared to its donor parent 93-11.Further investigation identified two quantitative trait loci(QTL)on chromosome 11,designated qGW11a and qGW11b,which contribute to 1000-grain weight with an additive effect.LOC_Os11g05690,encoding the amino acid permease OsCAT8,is the target gene of qGW11a.Overexpression of OsCAT8 resulted in decreased grain weight,while OsCAT8 knockout mutants exhibited increased grain weight.The 287-bp located within the OsCAT8 promoter region of 93-11 negatively regulates its activity,which is subsequently correlated with an increase in grain size and weight.These results suggest that OsCAT8 functions as a negative regulator of grain size and grain weight in rice.
文摘Background: Obesity has become a serious global public health challenge, given that it leads to various adverse health outcomes that include cardiovascular illnesses, diabetes, and certain types of cancer. The World Health Organization (WHO) has estimated that, at the end of 2022, 1 out of every 8 individuals were obese, and that the global adult obesity rates have over doubled since 1990, even as the adolescent obesity rates have quadrupled. Thus, as of 2022, nearly 2.5 billion adults, aged 18 years and above, were overweight, with 890 million being obese. Obesity and overweight incidence rate has been gradually increasing over the years, presenting significant challenges to the healthcare systems throughout the globe. In this regard, the objective of this systematic review was to evaluate the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. Methodology: To attain the above stated study objective, a systematic evaluation of previous studies was carried out, particularly studies that assessed the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. The authors have used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in the selection of eligible studies for inclusion in the study. Results: The findings indicate that lifestyle interventions resulted in 5% - 10% weight reduction and significant improvements in metabolic indicators, while pharmacotherapy (GLP-1 receptor agonists) achieved up to 15% weight reduction and considerable metabolic health benefits. Further, comparative studies show lifestyle modifications provide overall health benefits, while medication is necessary for non-responders. Conclusion: Individualized treatment strategies are crucial, and further research is needed on long-term consequences and combination therapies.